The Economic Impacts of ICD-9 to ICD-10 Health Indicator Coding System Transition in the Calgary Region
Bibliographic record
Abstract
IntroductionCoded data serves a critical part in the process of identifying the resource allocation required for each department in a hospital and for research purposes. This paper attempts a cost-benefit analysis of the transition from ICD-9 health indicator coding system to ICD-10 coding system and quantify the economic impacts. Objectives and ApproachThe hypothesis adopted by this paper is that the transition from ICD-9 to ICD-10 has been beneficial for the health system due better disease management, resulting in cost savings and facilitation of high quality health research. Analyzing the inflation-adjusted costs compared with the benefits accrued from implementing the new coding system would enable informed decision making for the stakeholders at government and other levels of health provision. The methodology involves constructing ‘benefit scenarios’ via analysis of existing literature and interviewing coding managers; costs are evaluated using data collected on re-training coders and productivity losses during the transition phase. ResultsAn example of a benefit scenario would take the form of cost savings associated with correctly identifying people with diabetes (due to coded charts), hence resulting in a decline in blood sugar (HbA1c) levels via better disease management. This in turn may cause reductions in other high blood-sugar related diseases and thus increase efficiency for government funding in the health care sector. Improved data quality in ICD-10 is expected to have resulted in gains from specificity due to increased sensitivity of data classification and grouping. Actual cost of re-training of coders and ICD-10 software provider fees are expected to be higher than the costs anticipated before ICD-10 implementation. Productivity losses in the transition phase are expected to have declined as coders became more adept at coding. Conclusion/ImplicationsAn economic evaluation proves to be a vital part of eliciting whether the transition to the newer method of coding, ICD-10, has been beneficial to the end users of the data. It is important to understand the efficiency of resource allocation to healthcare and the financial implications such investments entail.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".